During the last years, various methods and techniques have been used to
estimate the seafloor topography with high accuracy. The topography is
considered an important component for marine applications since several
activities and infrastructures are being carried out at the bottom, column
and surface of the sea. In this, study, freely and open distributed optical
satellite images were used to obtain bathymetric data for shallow waters,
(until 30 m below sea level) based on timeseries analysis of multispectral
Sentinel-2 datasets. The ratio transform algorithm was implemented for twelve
(12) monthly images covering thus a whole year. The different generated
Digital Terrain Models (DTMs) were compared with high resolution LIDAR
measurements. The results showed that bathymetry can be obtained from
satellite data within a Root Mean Square Error (RMSE) ranging from 1.39 up to
2.56 m while more accurate results were generated during the summer period.

Bathymetrical data are a crucial component of the marine area since several
activities and infrastructures are being carried out at bottom, column of
the sea and sea surface. The importance of bathymetry is quite high in areas
where there are shallow waters, such as harbors. Nowadays, various remote
sensing datasets are used to determine the topography of the seafloor with
high accuracy, including active sensors. Traditionally, the active sensors
used for estimating bathymetry are the echo sounders (Multi-Beam Echo
Sounders, Single-Beam Echo Sounders) or LIDAR devices installed on vessels
following a course along specially designed sailing lines that have a
specific geometry (Kearns and Breman, 2010). Beyond the fact that
these methods are very accurate, they are also very time consuming and
costly (McCaffrey,
1981; Sánchez-Carnero et al., 2012). Airborne LIDAR is another method
that has been implemented for estimating the bathymetry which revealed to be
cost effective compared onboard the vessels providing high area coverage
faster (Irish and Lillycrop, 1999;
Muirhead and Cracknell, 1986; Ogawa et al., 2017).

During the last years new studies related to Satellite-Derived Bathymetry
(SDB) emphasize the potential use of optical satellite remote sensing
sensors for bathymetric purposes. Indeed, analytical, semi-analytical and
empirical approaches may be implemented for the estimation of bathymetry of
up to 30 m depth. The analytical and semi-analytical approaches are
based on the physics of light's transmission in water using different
parameters of the atmosphere, water column and bottom material. By comparing
the three methods, the analytical and semi-analytical methods seem to be
more complicated than the empirical one, with more parameters in order to
extract bathymetrical data (Gao, 2009). Towards the
end of 1970s, it was demonstrated that bathymetry could be estimated over
clear shallow water using remote sensing with a multi-band log linear
algorithm (Lyzenga, 1978, 1980). Afterwards, this
analytical approach has been determined in various ways or with small
modifications to derive bathymetry (Conger et al., 2006;
Lyzenga et al., 2006; Maritorena et al., 1994; Philpot, 1989). An algorithm
for determining the depth of penetration (DOP) zones for every band and
calibrating depths presented by Jupp (1988). Also,
semi-analytical approaches were implemented, using models and methods for
retrieving bathymetry data (Adler-Golden et
al., 2005; Lee et al., 1999). Systematic errors are experienced by using the
analytical and semi-analytical approaches on the coastal areas containing
seagrass because of the reflectance which is lower on the neighboring deep
waters. This issue derives most probably from the shading which occurs
within the canopy (Dekker et al., 2007). Another method was
implemented, named ratio method, for the extraction of bathymetric data over
clear shallow water (Stumpf et
al., 2003), in which the authors argue that the ratio for two bands at the
constant depth will be the same, independently of the difference of bottom
albedo, and can be scaled with actual depths.

Over the years, many researches used several optical satellite imageries to
extract bathymetrical data. As, remote sensing technologies are continuously
upgrading, they are also expanding the horizons of researchers in the field
of bathymetry using high resolution satellite images (Said et al., 2017; Setiawan et al.,
2013). These multispectral sensors, provide a wide range of wavelength bands
which can be applied to these models, especially green and blue bands which
can penetrate up to 20 m underneath the sea surface in a clear water
condition (Sutanto, 1992).

Despite the medium spatial resolution, open and freely distributed data have
also attracted the interest of researchers. Indeed, during the years,
different studies carried out to estimate bathymetry using freely
distributed medium resolution (30 m pixel size) Landsat-7 ETM+ and
Landsat-8 LDCM satellites. The most common algorithms used in these studies
where the log linear (Lyzenga, 1978, 1980) and the
non-linear models (Stumpf et
al., 2003). For instance the non-linear algorithm was applied to Landsat
images in the area of the Dutch Wadden Sea (Ameland
Inlet, Misra et al., 2018) with a coefficient of
determination (R2) of 0.85 and Root-Mean-Square Error (RMSE) 3.12 m; on
Thousand island (Pushparaj and Hegde, 2017) with R2=0.90 and RMSE = 0.86 m for depths up to 10 m and at the coastal region of
Dakshina Kanada in India, Kastela bay in the middle Adriatic with R2 of
0.90 (Setiawan et al., 2017).

The exploitation of freely and open distributed datasets for bathymetric
purposes has been supported by the new capabilities of the Sentinel-2
sensors, having a 10 m resolution. Recently, the log linear and non-linear
methods were used for mapping the bathymetry, on the South Baltic Sea
(Chybicki, 2017). In that study, satellite
imageries from the Sentinel-2A sensor and soundings data were obtained by a
single-beam echo sounder and were used to estimate SDB. Afterwards, error
distribution and quality coefficient were calculated to describe the quality
of the retrieved SDB. Further, the retrieved SDB was calibrated with the
indicator of quality coefficient. In this way, more accurate results were
retrieved from SDB, proposing the implementation of this indicator on
mapping bathymetry using remote sensing. The best accuracy reported using
the ratio transform algorithm method was with an R2=0.91 and RMSE = 1.08 m. At the same time,
Kabiri (2017) used linear,
non-linear methods for bathymetric purposes on the Nayband bay, in Iran,
using two Sentinel-2 satellite imageries of February and March 2017. The
best accuracy was estimated to 1.89 and 2.04 m for months February and
March respectively, using the ratio transform algorithm.

More, recently the benefits of the Sentinel-2 imageries using big data cloud
platforms were presented by Traganos
et al. (2018). The Google Earth Engine, a cloud-based geospatial computing
platform, was used to acquire Sentinel-2 imageries for the period between 1 August and 31 December 2016, in four different locations at the eastern
Mediterranean basin. Analytical and empirical methods were implemented,
including the pseudo-invariant features for normalisation and the median 3×3 filter to smooth the remaining noise, hence increasing SDB accuracies. The
best accuracy spotted using the analytical method had a R2 of 0.90 and
RMSE = 1.67 m.

Though these studies have shown the great potentials of estimating
bathymetry using freely and open source satellite data such as Sentinel-2
images, there is still a research to be performed regarding the
identification of the best period throughout the year where bathymetric
errors are minimized. In addition, bathymetric studies based on optical
sensors are rare in the wider Eastern Mediterranean region and therefore
extensive research will benefit in the future. Even though satellite remote
sensing in this area was used in coastal areas for several purposes such as
in marine spatial planning, monitoring archaeological sites, quality of
coastal water etc. (Agapiou
et al., 2011; Hadjimitsis et al., 2010, 2013; Papoutsa et al., 2011) no
bathymetrical studies have been made. Currently through an existing funded
project named as “THAL-CHOR 2” (“ΘAΛ-XΩP 2” in Greek), bathymetric maps are being developed
through the use of the remote sensing which will be used for the
implementation of marine spatial planning in Cyprus.

For the framework of this study, optical Sentinel-2 satellite data were used
to extract bathymetric information in Cyprus. The present study is comprised
of five parts. Further to the introduction, the datasets and methodology
part follows, mentioning the field data of Multi-Beam Echo Sounders and
LIDAR instruments. Then, the empirical approach and the methodology used in
this study is described. The next part concentrates the results of the
applied methodology providing the correlation between observed depths and
SDB depths, twelve bathymetric maps. Finally, the paper ends with a
discussion and the conclusions.

2.1 Study site

The study site is located south west of the city of Limassol in Cyprus and
it covers 19 km2 with a coastline that is about 9.6 km in length
(Fig. 1). The site is located between 34∘41′38.4′′–34∘42′7.2′′ S and 33∘4′30′′–33∘10′44.4′′ E (WGS 84,
36N). On the coastlines of the study area, the soft substrate with sandy
beaches from shallow waters to a depth of twenty meters is gradually
becoming muddy (DoF, 2012). Also, breakwaters can be found on the
coasts of Limassol at a distance of 120 m from the shoreline. Cyprus has an
intense Mediterranean climate with hot dry summers, rainy, rather
changeable, winters and in autumn and spring season the weather conditions
are changing rapidly. The winds usually are light to moderate and rarely
unstable.

2.3 LIDAR in-situ data

The in-situ data were collected and analyzed by the Department of Land
Surveyors of Cyprus (DLS, 2014) – the official chartographic
provider of the country – covering water depths up to 50 m for an area of
approximately 508 km2. The data were acquired in two phases; the first
phase was conducted in 2012 when the DLS launched process mapping of the sea
with multi-beam echo sounders using a conventional vessel covering 238 km2; the second phase was conducted in 2014 using the airborne LIDAR
method covering 270 km2. The vertical accuracy as estimated by the DLS
is ±0.5 m for depths 0 to 20 m and ±1 m for up to 50 m.
Consequently, for this research, the DLS provided a set of 244 random
control points of 0 to 22 m depth which have been used for: (a) calibrating the bathymetric results obtain from the multispectral imageries
to absolute depths and (b) for training the ratio transform algorithm.
Figure 2, shows the overall geographical
distribution of the control points within the area of interest.

Figure 2Field data acquired by the Department of Land Survey in Cyprus.

Several methods have been implemented for the estimation of the bathymetry
using remote sensing. This research, gives emphasis in one of the most
commonly used methods (ratio transform algorithm) for deriving depths using
satellite imagery, (Stumpf et
al., 2003). In this method, the approach of Beer's Law was accepted which
considers that light attenuation in the water column increases exponentially
as depth increases (Gao, 2009). The log
transformation algorithm is related to the natural logarithm for reflectance
of two bands and actual depths.

Therefore, the bands used for estimating the bathymetry are the blue (440–540 nm), green (500–600 nm), red (600–700 nm) and near infrared
(700–800 nm) bands. The penetration in the blue and green part of the spectrum
is higher while the absorption of the electromagnetic radiation is
increasing as we move to the red part of the spectrum. The ratio transform
algorithm can be apply with bands having different water absorption and can
be applied in appropriate wavelengths of any sensor. Since blue and green
bands have lower absorption, their ratio should remain the same despite the
different bottom albedos at a constant depth. The values of these bands were
implemented on the following Eq. (1) to estimate bathymetry.

(1)Z=m1ln⁡R(λb)ln⁡R(λg)-m0

where Z is the satellite derived bathymetry depth, m0 is the offset for a
depth of 0 m, m1 is a coefficient to tune the model to the actual depth, and
R(λb) and R(λj) are the remote sensing radiances for
optical bands λb (blue band) and λg (green). For this
model, the bottom depth is estimated on the basis of light attenuation
phenomena, as the attenuation of the incoming shortwave radiation varies
spectrally.

The ratio transform algorithm was implemented to the multispectral imageries
listed in Table 1. Initially the multispectral
images were resampled by a referenced band with a 10 m spatial resolution,
using the SNAP software. Upon the selection of the area of interest, the
ArcGIS software was used where the shapefile was created and uploaded on the
SNAP software to mask satellite imageries. Following, each one of the masked
imageries classified the blue, green, red and near infrared band and then
separated land and water. After the pre-processing of satellite data, the
processed product was uploaded on the ENVI 5.3 software for estimating the
bathymetry using the log transformation algorithm. The median filter, a
nonlinear digital filtering technique, used for removing the noise from
images, was applied to this study. A median filter of 7×7 was used to
succeed the reduction of the noise of high frequency. The following figure
(Fig. 3), shows the processing steps for deriving
bathymetry on the study area.

Following the above methodology, twelve bathymetry maps, one for each month,
were estimated. The median filter has been implemented to eliminate
parameters of deterioration in estimating bathymetry such as turbidity,
waves, sun glint and sky glint. Following a time series regression analyses,
the RMSE and Mean Absolute Errors (MAE) have been calculated.

4.1 Time series regression analyses of Bathymetrical data

In order to estimate the correspondence of the satellite bathymetric model
with the Lidar datasets a regression analysis has been performed for each
monthly DTM using the linear model. The overall results are found in
Fig. 4. Twelve scatterplots have been designed
with the x-axis representing the LIDAR points, while the y-axis represented
the estimated bathymetry points derived from the Sentinel-2 images. Based on
the linear regression analysis the R2 coefficient was also estimated
for each case. According to the results, strong correlation is observed in
shallow waters (less than 12 m) compared to deeper areas (more than 12 m) .
Scatterplots of January and February show that R2 is relatively high up
to 0.90 while R2 for the next couple of months is estimated to be
between 0.85 and 0.88. During summer season R2 value is increased while
June provides the best linear fit with a value of 0.95. Lastly, the R2
value for autumn, seems to have a downward trend and is the same with
winter's one. The scattering starts from 12 m depth, as also clearly showed
for the analysis during November. This is reflected to the R2 value
which is equal to the 0.81 and it is the less precise value from the
results.

Figure 4Correlation between observed depths and SDB depths obtained for
each month.

In order to compare the accuracy of the time series extracted results, two
statistical parameters were taken into consideration along with correlation
coefficient R2, RMSE and MAE. The equations for the estimation are
given in the following equations:

(2)RMSE=1n∑J=11|ZSDB-ZFB|(3)MAE=1n∑J=11(ZSDB-ZFB)2

where n is the number the field points, ZSDB is the satellite derived
bathymetry depth and ZFB is the field points depth.

The overall results regarding the RMSE and MAE errors are shown on
Table 2. As resulted from the table below, the best
results were the ones derived for month June where the bathymetry resulted
with RMSE to be 1.39 m and MAE 1.05 m. On the other hand, the less precise
results having a RMSE of 2.56 m and a MAE of 1.64 m seem to be for month
November.

4.2 Results of bathymetrical data

This section demonstrates and compares the various SDB maps produced from
the Sentinel-2 images with the LIDAR datasets.
Figure 5 below, shows the bathymetrical results of
the study area for each month. Overall, the best accuracy is evident during
the summer period with RMSE of 1.39 m and MAE of 1.05 m while the autumn
period gives the lowest precision with RMSE of 2.56 m and MAE of 1.64 m. The
latest may be due to bad weather conditions and the sea state. It is worth
noted that bathymetric maps obtained during the period November December
encountered error in the South-East. These errors are probably related to
cloud coverage of the satellite images.

As it can be seen from the presentation of the results, the period used for
estimating bathymetry effects the overall performance of the results. The
records provided by the DLS have a 5-year difference from the downloading of
the satellite images which is considered to be a very long timeframe, thus
data have limitations on the accuracy. In order to compare the estimated
bathymetry with the in situ data, comparison maps between the bathymetric
satellite DTMs and LIDAR DTMs were created and can been seen on
Fig. 6.

Based on these findings it is evident that during winter and autumn periods
errors can be more than 5 m. Higher errors of more than 4 m are
reported in deeper waters for all seasons with the exception of the summer
season. In shallow waters the same performance despite the period selected
was observed. In addition, in sea areas with breakwaters the differences
range from 1 up to 2.5 m.

Bathymetrical profiles were also created in areas that showed frequently
encountered errors. Optically observing the maps with the errors'
allocation, it can be pointed out that there are more frequent errors in the
southwest of the study area. Bathymetrical profile lines of each month were
used in order to interpolate the bathymetrical data. Besides, another
profile was created, which is based on the LIDAR control points in order to
be compared with those of SDB. The above described method was followed with
the use of ArcGIS v10.2 Software for the analysis of the diagrams which have
been exported by the software, the user has resulted to the diagram depicted
in Fig. 7

A very good correlation between the profiles is perceptible to
Fig. 7. For a horizontal distance from the
coastline up to 200 m with depths of 5 m, a noise was observed between SDB
and the interpolated bathymetry from the field points. This might be due to
the wave collapse on the shoreline and the breakwaters which are located 120 m from the shoreline. On the other hand, on the coastal distance of 200 to
400 m in the area with depth of 5–10 m seems to be well correlated except
the month of January. Moreover, the correlation of the bathymetric data with
distance ranging from 400–800 m seems to be good except from the months of
March, April and December. Lastly, the distance ranging from 800–1200 m with
20 m depth has a gradual upward shift of from the fields' points' surface.
This might be due to the attenuation of the light as it passes through the
water column and the deterioration of solar radiation at various depths in
the water. Another reason could be the time difference between satellite
images and field points.

On the current study, twelve images, one for each month, have been used for
estimating the bathymetry in the south area of Cyprus. During the analysis,
the overall results indicate an error ranging from 1.39 up to 2.56 m.
Comparing the bathymetric data, it is evident, summer months provide the
best results. Indeed, the best accuracy of bathymetry data is found in
Sentinel image acquired on June with a R2=0.94, RMSE = 1.39 m and MAE = 1.05 m. Also, a good correlation between the bathymetric maps of May's to
Augusts' (Fig. 5) with accuracies of the RMSE
ranging from 1.39 to 1.70 m was observed. In contrast errors of more than
5 m during the autumn and winter months, especially in October and November
have been reported in deeper waters. The accuracy of the study is considered
acceptable, compared to other studies using the same method and satellite
imageries such as those of Chybicki (2017), Kabiri (2017) and Traganos and Reinartz (2017). The correlation
between situ data and predicted depths, is not the ideal and this may be due
the limitations of accuracy that have LIDAR data. Also, a gradual increase
in the accuracy of the results was noted in the months of June and December,
months of the winter and summer solstice.

An overall analysis indicates high accuracy of the estimated depths in
shallow waters which then reduces in deeper waters. Studying the
bathymetrical profiles shows a good correlation between the observed and
extracted depths from 5 to 12 m while there were deviations in deep waters.
The correlation between the in situ data and results can be found in other
studies that show better results in shallow waters (Ehses and Rooney, 2015;
Pushparaj and Hegde, 2017).

Further to this study, comparison maps were created. The outcome of the maps
shows the difference between the estimated bathymetry with the in-situ data.
These comparison maps show good correlation to the shallow waters, with the
differences ranging from 0.5 to 1.5 m. It is also worth to notice that
breakwaters in the study area might have deteriorated the results as shown
in the comparison maps.

Bathymetry is an important factor for several applications such as
navigation, marine environmental protection, coastal engineering etc. During
the last years many studies implemented SDB using empirical, analytical and
semi-analytical approaches, aiming to investigate the potential use of
optical satellite datasets. A critical aspect highlighted on most of these
studies refers to the atmospheric and sea conditions of the area including
water column and bottom material characteristics. It is therefore important
to analyse an optical satellite image with the best possible conditions
(cloud coverage, waves, turbidity etc.). For this reason, the study was
focused to the identification of the best period for the estimation of the
bathymetry following a multi-temporal approach.

Therefore, the study was focused on the analysis of the multi-temporal
Sentinel-2 satellite imageries in different periods using the empirical
method ratio transform algorithm. The bathymetrical data were extracted
using SDB and statistically analyzed for one year. The overall results show
that the bathymetry accuracy tends to fluctuate from period to period
significantly and therefore this should be taken into consideration in
combination with the weather conditions. The latest is due to the fact that
turbidity and waves affect the attenuation of the light observed during bad
weather conditions and strong winds.

For the purpose of improving the current study, a comparison timeseries
analysis exploiting ESA's Sentinel Scientific Data hub will be performed in
the near future. Based on the given high temporal resolution of
Sentinel-L2A-B sensors, a multitemporal analysis during the summer season
(optimum period for bathymetric purposes as evidence in this paper) will be
investigated by authors in the near future. The purpose of that study is to
identify fluctuations within a specific season (i.e. summer), following
other methods as well, aiming to minimize even more the errors and produce
more accurate bathymetric maps. In addition, this will allow isolate the
optimum window for exploiting the free and open satellite data.

The original field data presented in the paper are
available by contacting the Department of Land Surveyors of Cyprus. The
original data underlying the satellite imageries are available in the
Copernicus minor site.

This article is part of the special issue “Earth surveillance
and space-based monitoring of the environment: integrated approaches”. It is
a result of the EGU General Assembly 2018, Vienna, Austria, 8–13 April
2018.

This research was supported by project entitled: “Cross-Border Cooperation
for Implementation of Maritime Spatial Planning” referred as “THAL-CHOR
2” (“ΘAΛ-XΩP 2” in
Greek) and is co-funded by the European Regional Development Fund (ERDF),
under the Cross-Border Cooperation Programme “INTERREG V-A Greece-Cyprus
2014–2020”. Thanks, are given to the Remote Sensing and Geo-Environment
Lab, of the Cyprus University of Technology. Also, acknowledgements are
given to Department of the Land Survey of Cyprus, for providing soundings of
bathymetry. Sentinel-2A imagery was acquired for the ESA's Sentinel
Scientific Data hub.

Freely and open distributed optical satellite images used to obtain bathymetric data for shallow waters based on timeseries analysis of multispectral Sentinel-2 datasets. The ratio transform algorithm was implemented for twelve monthly images covering a year. Bathymetric maps were generated and compared with LIDAR measurements. The results showed that bathymetry can be obtained from satellite data within a Root Mean Square Error while more accurate results were generated during the summer.

Freely and open distributed optical satellite images used to obtain bathymetric data for shallow...